CS330 - Assignment Template
Author
Andre dos Santos
Last Updated
4 年前
License
Creative Commons CC BY 4.0
Abstract
Assignment Template for CS330 - Introduction to Operating Systems
Assignment Template for CS330 - Introduction to Operating Systems
%%%%%%%%%%%%%%%%% DO NOT CHANGE HERE %%%%%%%%%%%%%%%%%%%%
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\documentclass[twoside,11pt]{article}
%%%%% PACKAGES %%%%%%
\usepackage{pgm2016}
\usepackage{amsmath}
\usepackage{algorithm}
\usepackage[noend]{algpseudocode}
\usepackage{subcaption}
\usepackage[english]{babel}
\usepackage{paralist}
\usepackage[lowtilde]{url}
\usepackage{fixltx2e}
\usepackage{listings}
\usepackage{color}
\usepackage{hyperref}
\usepackage{auto-pst-pdf}
\usepackage{pst-all}
\usepackage{pstricks-add}
%%%%% MACROS %%%%%%
\algrenewcommand\Return{\State \algorithmicreturn{} }
\algnewcommand{\LineComment}[1]{\State \(\triangleright\) #1}
\renewcommand{\thesubfigure}{\roman{subfigure}}
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\definecolor{codepurple}{rgb}{0.58,0,0.82}
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\lstdefinestyle{mystyle}{
backgroundcolor=\color{backcolour},
commentstyle=\color{codegreen},
keywordstyle=\color{magenta},
numberstyle=\tiny\color{codegray},
stringstyle=\color{codepurple},
basicstyle=\footnotesize,
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breaklines=true,
captionpos=b,
keepspaces=true,
numbers=left,
numbersep=5pt,
showspaces=false,
showstringspaces=false,
showtabs=false,
tabsize=2
}
\lstset{style=mystyle}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% }
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\newcommand\course{CS 330-001}
\newcommand\courseName{Introduction to Operating Systems}
\newcommand\semester{Winter 2020}
\newcommand\assignmentNumber{1} % <-- ASSIGNMENT #
\newcommand\studentName{Your Name} % <-- YOUR NAME
\newcommand\studentEmail{email@uregina.ca} % <-- YOUR NAME
\newcommand\studentNumber{200XXYYZZ} % <-- STUDENT ID #
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%{
\ShortHeadings{University of Regina - \course ~~ \courseName}{\studentName - \studentNumber}
\firstpageno{1}
\begin{document}
\title{Assignment \assignmentNumber}
\author{\name \studentName \email \studentEmail \\
\studentNumber
\addr
}
\maketitle
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% }
This is an example of how to cite:
\cite{darwiche00} proposed a new approach for inference in Bayesian networks (BNs) \citep{pear88} based on partial differentiation called \emph{Arithmetic Circuits} \citep{darwiche00}.
This template is organized as follows.
In Section \ref{sec:background}, $\ldots$
\section{Example Section}
\label{sec:background}
%%EXAMPLE EQUATION
A \emph{joint probability distribution} (JPD) is function $P$ on the Cartesian product $V$ of the variable domains such that the following two conditions hold:
\begin{align}
&0 \leq P(v) \leq 1.0\text{ for each configuration $v \in V$; and}\\ \label{eq_01}
&\sum_{v \in V}{P(v)} = 1.0
\end{align}
Referring to Equation \ref{eq_01} here.
%EXAMPLE MULTIPLE TABLES TABLE
\begin{table}[!htb]
\caption{Table (a) provides the prior probability of variable $A$ and Table (b) provides the conditional probability of $B$ given $A$.}
\begin{subtable}{.5\linewidth}
\centering
\caption{}
\begin{tabular}{c|c}
$A$ & $P(A)$ \\ \hline
1 & 0.3 \\
0 & 0.7
\end{tabular}
\label{sub_table}
\end{subtable}%
\begin{subtable}{.5\linewidth}
\centering
\caption{}
\begin{tabular}{cc|c}
$A$ & $B$ & $P(B|A)$ \\ \hline
1 & 1 & 0.1 \\
1 & 0 & 0.9 \\
0 & 1 & 0.8 \\
0 & 0 & 0.2 \\
\end{tabular}
\end{subtable}
\label{subtb:pa}
\end{table}
Referring to Table \ref{subtb:pa} (\subref{sub_table}) here.
\subsection{Example Subsection}
%%EXAMPLE MATH INLINE
This is how you use math items inline:
($\theta_a \star \lambda_a$),
($\theta_{ab} \star \lambda_{{b}}$),
($\theta_{a{\bar b}} \star \lambda_{{\bar b}}$),
($\lambda_{{b}} \star \theta_{{\bar a}b}$),
($\lambda_{{\bar b}} \star \theta_{{\bar a}\bar{b}}$), and
($\lambda_{{\bar a}} \star \theta_{{\bar a}}$).
Referring to Figure \ref{fig:deri}.
\newpage %goes to a new page
%% EXAMPLE ALGORITHM
\begin{lstlisting}[language=python]
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Layer, Activation
import time
import tensorflow as tf
f = open("results.csv", "w")
INPUT_SIZE = 10
OUTPUT_SIZE = INPUT_SIZE
nb_class = 3
batch_size = 128
nb_epoch = 40
np.random.seed(123)
X_train = np.random.rand(INPUT_SIZE, nb_class)
Y_train = np.random.rand(OUTPUT_SIZE, nb_class)
X_test = np.random.rand(INPUT_SIZE)
Y_test = np.random.rand(OUTPUT_SIZE)
for i in range(1,51):
start_time = time.time()
model = Sequential()
model.add(Dense(INPUT_SIZE, input_shape=(nb_class,)))
model.add(Activation('linear'))
model.add(Dense(OUTPUT_SIZE))
model.add(Activation('linear'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
final_time = time.time()
diff_time = final_time - start_time
f.write(str(i)+","+str(diff_time)+","+"\n")
f.close()
\end{lstlisting}
\begin{table}[!h]
\begin{center}
\caption{Comparison of parallel and serial solutions for ACs with 50 runs each}
% \resizebox{\columnwidth}{!}{
\begin{tabular}{ l | cc }
Solution & Time average & Standard deviation \\ \hline
Serial & 0.1610 & 0.0536 \\
Parallel & 0.0434 & 0.0082
\label{tab:bench_bns}
\end{tabular}
% }
\end{center}
\end{table}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.7\columnwidth]{figures/graph.png}
\caption{Comparison between CPU and GPU implementation of AC compiling.}
\label{fig:deri}
\end{center}
\end{figure}
\section{Conclusion}
\label{sec:conc}
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%% HERE IS WHERE THE REFERENCES IS INCLUDED
%% TO ADD NEW ONES, GO TO THE FILE references.bib IN THE REFERENCES FOLDER AND ADD IN bibtex FORMAT
\vskip 0.2in
\bibliography{references/references}
\end{document}